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In this study, a new analytical techniques is proposed for seeking policy alternatives aimed at objectives of TDM, increasing the transit rideshare. Determinants of travel mode such as personal characteristics, lifestyle, and urban spatial characteristics are interdependent and have combined effect on decision. In addition, individuals, groups, and regional characteristics have interdependencies at different levels. Unlike traditional regression analysis, hierarchical analysis model has the advantage of identifying interdependencies and complex relationship between the combined impact factors. This analysis technique is expected to be a significant contribution to seek a more efficient TOD policy.

A general equilibrium multiperiod linear programming model of urban land use is used to identify reverse commutation as a rational response to economic change. Information about future economic conditions is complete, and capital is replaceable at finite cost. Export mixes are defined exogenously, and temporal shifts between activities of different land intensities are shown to induce reverse commutes as one way of avoiding the cost of land use change.

Many optimization procedures presume the availability of an initial approximation in the neighborhood of a local or global optimum. Unfortunately, finding a set of good starting conditions is itself a nontrivial proposition. Our previous papers [1,2] describe procedures that use simple and recurrent associative memories to identify approximately solutions to closely related linear programs. In this paper, we compare the performance of a recurrent associative memory to that of a feed-forward neural network trained with the same data. The neural network's performance is much less promising than that of the associative memory. Modest infeasibilities exist in the estimated solutions provided by the associative memory, but the basic variables defining the optimal solutions to the linear programs are readily apparent.

One of the least satisfactory features of regional analysis is the gulf between the studies of regional economic change and the study of regional spatial structure. Recent regional economic analysis concerns empirical and theoretical developments in growth theory, econometric modelling, and input-output techniques; but are rarely concerned with spatial structure. Similarly, studies of spatial structure are generally undertaken in a static context seldom related to the process of regional economic change. We contend the sectoral composition of a region’s economy exerts an important influence on the spatial structure of the region.